• East Asian mathematical journal
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• 제30권3호
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• pp.355-360
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• 2014

Let D be a finite digraph with the vertex set V (D) and the arc set A(D). A function f : $V(D){\rightarrow}\{-1,\;1\}$ defined on the vertices of a digraph D is called a bad function if $f(N^-(v)){\leq}1$ for every v in D. The weight of a bad function is $f(V(D))=\sum\limits_{v{\in}V(D)}f(v)$. The maximum weight of a bad function of D is the the negative decision number ${\beta}_D(D)$ of D. Wang [4] studied several sharp upper bounds of this number for an undirected graph. In this paper, we study sharp upper bounds of the negative decision number ${\beta}_D(D)$ of for a digraph D.

• 보건행정학회지
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• 제3권2호
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• pp.159-176
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• 1993

The utilization of outpatient care services involves two steps of sequential decisions. The first step decision is about whether to initiate the utilization and the second one is about how many more visits to make after the initiation. Presumably, the initiation decision is largely made by the patient and his or her family, while the number of additional visits is decided under a strong influence of the physician. Implication is that the analysis of the outpatient care utilization requires to specify each of the two decisions underlying the utilization as a distinct stochastic process. This paper is concerned with the number of physician visits, which is, by definition, a discrete variable that can take only non-negative integer values. Since the initial visit is considered in the analysis of whether or not having made any physician visit, the focus on the number of visits made in addition to the initial one must be enough. The number of additional visits, being a kind of count data, could be assumed to exhibit a Poisson distribution. However, it is likely that the distribution is over dispersed since the number of physician visits tends to cluster around a few values but still vary widely. A recently reported study of outpatient care utilization employed an analysis based upon the assumption of a negative binomial distribution which is a type of overdispersed Poisson distribution. But there is an indication that the use of Poisson distribution making adjustments for over-dispersion results in less loss of efficiency in parameter estimation compared to the use of a certain type of distribution like a negative binomial distribution. An analysis of the data for outpatient care utilization was performed focusing on an assessment of appropriateness of available techniques. The data used in the analysis were collected by a community survey in Hwachon Gun, Kangwon Do in 1990. It was observed that a Poisson regression with adjustments for over-dispersion is superior to either an ordinary regression or a Poisson regression without adjustments oor over-dispersion. In conclusion, it seems the most approprite to assume that the number of physician visits made in addition to the initial visist exhibits an overdispersed Poisson distribution when outpatient care utilization is studied based upon a model which embodies the two-part character of the decision process uderlying the utilization.

• ETRI Journal
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• 제39권5호
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

• 기술혁신학회지
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• 제17권3호
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• pp.449-466
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• 2014

그동안 기업의 R&D 투자와 기술혁신 성과의 관계에 관해 여러 연구가 있었다. 하지만 지식생산과정에 수반되는 기업의 의사결정 과정은 효과적으로 분석에 반영되지 못하였다. 특히 기업의 특허성과를 분석함에 있어 포와송모형의 한계에 대응해 최근 연구는 음이항모형을 적용해 극복하고자 했지만 기업의 선택과정을 분석하는데는 한계가 있다. 본 논문은 특허권 정보에 내재된 기업체의 의사결정 과정을 보다 효과적으로 반영하는 실증모형을 제시하고, 사업체조사 결과를 적용해 분석하였다. 특히 기업의 대표적 R&D 성과인 특허에 주목하여 특허 출원건수의 결정과정을 살펴보았다. 분석 결과, 과산포의 존재를 확인할 수 있었고, 허들모형과 일반적인 음이항모형의 결과에 유의한 차이가 있음을 제시하였다. 더불어 Wald-검정을 통해 허들의 설정이 타당하였고, 기업의 특허성과 분석에 있어서 기업의 선택과정을 고려할 필요가 있음을 보였다.

• 한국인공지능학회지
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• 제9권1호
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• pp.21-27
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• 2021

In this Paper, Since the 1990s, Korea's credit card industry has steadily developed. As a result, various problems have arisen, such as careless customer information management and loans to low-credit customers. This, in turn, had a high delinquency rate across the card industry and a negative impact on the economy. Therefore, in this paper, based on Azure, we analyze and predict the delinquency and delinquency periods of credit loans according to gender, own car, property, number of children, education level, marital status, and employment status through linear regression analysis and enhanced decision tree algorithm. These predictions can consequently reduce the likelihood of reckless credit lending and issuance of credit cards, reducing the number of bad creditors and reducing the risk of banks. In addition, after classifying and dividing the customer base based on the predicted result, it can be used as a basis for reducing the risk of credit loans by developing a credit product suitable for each customer. The predicted result through Azure showed that when predicting with Linear Regression and Boosted Decision Tree algorithm, the Boosted Decision Tree algorithm made more accurate prediction. In addition, we intend to increase the accuracy of the analysis by assigning a number to each data in the future and predicting again.

• Journal of applied mathematics & informatics
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• 제26권3_4호
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• pp.767-778
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• 2008

Comparison between two or more fuzzy numbers, along with their ranking, is an important subject discussed in scholarly articles. We endeavor in this paper to present a simple yet effective parametric method for comparing fuzzy numbers. This method offer significant advantages over similar methods, in comparing intersected fuzzy numbers, rendering the comparison between fuzzy numbers possible in different decision levels. In the process, each fuzzy number will be given a parametric value in terms of $\alpha$, which is dependent on the related $\alpha$-cuts. We have compared this method to Cheng's centroid point method [5] (The relation of calculating centroid point of a fuzzy number was corrected later on by Wang [12]). The proposed method can be utilized for all types of fuzzy numbers whether normal, abnormal or negative.

• Journal of Electrical Engineering and Technology
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• 제1권4호
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• pp.423-426
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• 2006

Distance relays achieve selective tripping by measurement of all short circuit fault conditions inside set reaches. The direction of the fault, forward or reverse is commonly determined with a dedicated measurement to ensure selectivity under all conditions. For the direction decision (measurement) a number of alternatives are available. This paper describes a loop selective direction measurement and illustrates by means of a typical fault why this is superior to a non loop selective direction measurement such as that based on negative sequence quantities.

• 정보처리학회논문지A
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• 제9A권3호
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• pp.363-370
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• 2002

OPKFDD(Ordered Pseudo-Kronecker Functional Decision Diagram)는 각 노드에서 다양한 확장방법(decomposition)을 취할 수 있는 Ordered-DD(Decision Diagram)의 한 종류로서 각 노드마다 Shannon, positive Davio, 그리고 negative Davio 확장중의 하나를 사용하도록 하며 다른 종류의 DD와 비교해서 작은 수의 노드로 함수를 표현할 수 있다. 그러나 각 노드마다 각기 다른 확장 방법을 선택할 수 있는 특징 때문에 입력 노드에 대한 확장 방법의 결정에 의해서 OPKFDD의 크기가 좌우되며 최소의 노드 수를 갖는 OPKFDD의 구성은 매우 어려운 문제로 알려져 있다. 본 논문에서는 DD 크기의 기준을 노드 수로 하여 기존의 OBDD(Ordered Binary Decision Diagram) 자료구조에서 각 노드의 확장방법을 결정하는 직관적(heuristic)인 방법을 제시하고, 주어진 입력변수 순서에 대해서 각 노드의 확장 방법을 결정하는 알고리즘을 제안하고 실험 결과를 제시한다.

• 충청수학회지
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• 제19권4호
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• pp.349-356
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• 2006

The aim of this paper is to endow a monoid structure on the set S of all oriented knots(links) under the operation ${\biguplus}$, called addition of knots. Moreover, we prove that there exists a homomorphism of monoids between ($S_d,\;{\biguplus}$) to (N, +), where $S_d$ is a subset of S with an extra condition and N is the monoid of non negative integers under usual addition.

• Journal of Information Technology Applications and Management
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• 제28권1호
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• pp.33-41
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• 2021

In this study, we estimated the number of secondary COVID-19 infections caused by students with potential transmission potential home. When the existing Monte Carlo method was applied to Korean data, the average number of household members of the second COVID-19 infected was predicted. The summary of this study is as follows. First, in general, the number of secondary infections by students returning home from school is greatly influenced by the virus infection rate of each student group they contact while returning home from school. Korea-based empirical research on this is needed. Second, the number of secondary infections by Korean students was relatively lower than that of previous studies. This can be interpreted as being due to the domestic furniture structure. Third, unlike previous studies that assumed the distribution of secondary infected individuals as normal distribution, assuming a negative binomial distribution, the number of secondary infected individuals was sensitively changed according to the estimated parameters. Interpretation of this result shows that the number of secondary infections may vary depending on the time of decision making, the target region, and the target student group. Finally, according to the results of this analysis, a proposal was made to support education policy decisions.